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Measuring Ambient Population from Location-Based Social Networks to Describe Urban Crime

机译：通过基于位置的社交网络测量环境人口来描述城市犯罪

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Recently, a lot of attention has been given to crime prediction, both by the general public and by the research community. Most of the latest work has concentrated on showing the potential of novel data sources like social media, mobile phone data, points of interest, or transportation data for the crime prediction task and researchers have focused mostly on techniques from supervised machine learning to show their predictive potential. Yet, the question remains if indeed this data can be used to better describe urban crime. In this paper, we investigate the potential of data harvested from location-based social networks (specifically Foursquare) to describe urban crime. Towards this end, we apply techniques from spatial econometrics. We show that this data, seen as a measurement for the ambient population of a neighborhood, is able to further describe crime levels in comparison to models built solely on census data, seen as measurement for the resident population of a neighborhood. In an analysis of crime on census tract level in New York City, the total number of incidents can be described by our models with up to R~2 = 56%, while the best model for the different crime subtypes is achieved for larcenies with roughly 67% of the variance explained.
机译：最近，公众和研究界都对犯罪预测给予了极大关注。最新的工作大部分集中在显示新颖数据源（如社交媒体，手机数据，兴趣点或交通数据）在犯罪预测任务中的潜力，研究人员主要集中在有监督的机器学习技术上以显示其预测能力。潜在。然而，问题仍然在于，是否确实可以使用这些数据来更好地描述城市犯罪。在本文中，我们调查了从基于位置的社交网络（特别是Foursquare）收集的数据描述城市犯罪的潜力。为此，我们应用了空间计量经济学的技术。我们表明，与仅基于人口普查数据构建的模型相比，该数据被视为对社区周围人口的度量，能够进一步描述犯罪水平，被视为对社区居民的度量。通过对纽约市人口普查区域犯罪的分析，可以用我们的模型描述事件的总数，R〜2 = 56％，而对于不同犯罪亚型的最佳模型则可以通过大约解释了67％的方差。

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